


import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)



def       _lowerCAmelCase    (				lowerCAmelCase_				:Union[str, Any]		,    lowerCAmelCase_				:Dict				)->List[str]:
   '''simple docstring'''






   snake_case_            =       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
   snake_case_            =       Image.open(requests.get(_lowercase		,    stream=_lowercase				).raw				).convert("RGB"				)

   snake_case_            =       transforms.Compose(
       [
           transforms.Resize((image_size, image_size)		,    interpolation=InterpolationMode.BICUBIC				),
           transforms.ToTensor(),
           transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3)		,    (0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)				),
       ]				)
   snake_case_            =       transform(_lowercase				).unsqueeze(0				).to(_lowercase				)
   return image



def       _lowerCAmelCase    (				lowerCAmelCase_				:Dict				)->List[Any]:
   '''simple docstring'''






   if "visual_encoder" in key:
      snake_case_            =       re.sub("visual_encoder*"		,    "vision_model.encoder"		,    _lowercase				)
   if "blocks" in key:
      snake_case_            =       re.sub(r"blocks"		,    "layers"		,    _lowercase				)
   if "attn" in key:
      snake_case_            =       re.sub(r"attn"		,    "self_attn"		,    _lowercase				)
   if "norm1" in key:
      snake_case_            =       re.sub(r"norm1"		,    "layer_norm1"		,    _lowercase				)
   if "norm2" in key:
      snake_case_            =       re.sub(r"norm2"		,    "layer_norm2"		,    _lowercase				)
   if "encoder.norm" in key:
      snake_case_            =       re.sub(r"encoder.norm"		,    "post_layernorm"		,    _lowercase				)
   if "encoder.patch_embed.proj" in key:
      snake_case_            =       re.sub(r"encoder.patch_embed.proj"		,    "embeddings.patch_embedding"		,    _lowercase				)

   if "encoder.pos_embed" in key:
      snake_case_            =       re.sub(r"encoder.pos_embed"		,    "embeddings.position_embedding"		,    _lowercase				)
   if "encoder.cls_token" in key:
      snake_case_            =       re.sub(r"encoder.cls_token"		,    "embeddings.class_embedding"		,    _lowercase				)

   if "self_attn" in key:
      snake_case_            =       re.sub(r"self_attn.proj"		,    "self_attn.projection"		,    _lowercase				)

   return key



@torch.no_grad()
def       _lowerCAmelCase    (				lowerCAmelCase_				:Tuple		,    lowerCAmelCase_				:Optional[Any]=None				)->int:
   '''simple docstring'''






   if config_path is not None:
      snake_case_            =       BlipConfig.from_pretrained(_lowercase				)
   else:
      snake_case_            =       BlipConfig(projection_dim=512		,    text_config={}		,    vision_config={}				)

   snake_case_            =       BlipForConditionalGeneration(_lowercase				).eval()

   snake_case_            =       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"

   snake_case_            =       blip_decoder(pretrained=_lowercase		,    image_size=384		,    vit="base"				)
   snake_case_            =       pt_model.eval()

   snake_case_            =       pt_model.state_dict()
   for key in modified_state_dict.copy():
      snake_case_            =       modified_state_dict.pop(_lowercase				)
      snake_case_            =       rename_key(_lowercase				)
      snake_case_            =       value

   hf_model.load_state_dict(_lowercase				)

   snake_case_            =       384
   snake_case_            =       load_demo_image(image_size=_lowercase		,    device="cpu"				)
   snake_case_            =       BertTokenizer.from_pretrained("bert-base-uncased"				)
   snake_case_            =       tokenizer(["a picture of"]				).input_ids

   snake_case_            =       hf_model.generate(_lowercase		,    _lowercase				)

   assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

   snake_case_            =       hf_model.generate(_lowercase				)

   assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

   if pytorch_dump_folder_path is not None:
      hf_model.save_pretrained(_lowercase				)

   # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
   snake_case_            =       (
       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
   )

   snake_case_            =       blip_vqa(pretrained=_lowercase		,    image_size=_lowercase		,    vit="base"				)
   vqa_model.eval()

   snake_case_            =       vqa_model.state_dict()
   for key in modified_state_dict.copy():
      snake_case_            =       modified_state_dict.pop(_lowercase				)
      snake_case_            =       rename_key(_lowercase				)
      snake_case_            =       value

   snake_case_            =       BlipForQuestionAnswering(_lowercase				)

   hf_vqa_model.load_state_dict(_lowercase				)

   snake_case_            =       ["How many dogs are in this image?"]
   snake_case_            =       tokenizer(_lowercase		,    return_tensors="pt"				).input_ids

   snake_case_            =       hf_vqa_model.generate(_lowercase		,    _lowercase				)
   print(tokenizer.decode(answer[0]				)				)

   assert tokenizer.decode(answer[0]				) == "[UNK] 1 [SEP]"
   if pytorch_dump_folder_path is not None:
      hf_vqa_model.save_pretrained(pytorch_dump_folder_path + "_vqa"				)

   snake_case_            =       "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

   snake_case_            =       blip_itm(pretrained=_lowercase		,    image_size=_lowercase		,    vit="base"				)
   itm_model.eval()

   snake_case_            =       itm_model.state_dict()
   for key in modified_state_dict.copy():
      snake_case_            =       modified_state_dict.pop(_lowercase				)
      snake_case_            =       rename_key(_lowercase				)
      snake_case_            =       value

   snake_case_            =       BlipForImageTextRetrieval(_lowercase				)

   snake_case_            =       ["A picture of a woman with a dog sitting in a beach"]
   snake_case_            =       tokenizer(
       _lowercase		,    return_tensors="pt"		,    padding="max_length"		,    truncation=_lowercase		,    max_length=35		,    ).input_ids

   hf_itm_model.load_state_dict(_lowercase				)
   hf_itm_model.eval()

   snake_case_            =       hf_itm_model(_lowercase		,    _lowercase		,    use_itm_head=_lowercase				)
   snake_case_            =       hf_itm_model(_lowercase		,    _lowercase		,    use_itm_head=_lowercase				)

   assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
   assert torch.nn.functional.softmax(out_itm[0]		,    dim=1				)[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

   if pytorch_dump_folder_path is not None:
      hf_itm_model.save_pretrained(pytorch_dump_folder_path + "_itm"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:Optional[int]			=  argparse.ArgumentParser()
   parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
   parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
   SCREAMING_SNAKE_CASE						:int			=  parser.parse_args()

   convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)








"""simple docstring"""



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
		raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
		raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
lowercase_							=							logging.get_logger(__name__)

lowercase_							=							'Hello, World!'
lowercase_							=							'en_XX'
def      UpperCAmelCase     (       _lowercase :   str			,   _lowercase :   str			,   _lowercase :   bool	)						-> Optional[int]:


	"""simple docstring"""




	lowerCAmelCase_      							=			Path('''data_bin'''	)
	lowerCAmelCase_      							=			FairseqXmodModel.from_pretrained(
	    model_name_or_path=str(Path(_lowercase	).parent	)			,   checkpoint_file=Path(_lowercase	).name			,   _name='''xmod_base'''			,   arch='''xmod_base'''			,   task='''multilingual_masked_lm'''			,   data_name_or_path=str(_lowercase	)			,   bpe='''sentencepiece'''			,   sentencepiece_model=str(Path(_lowercase	).parent / '''sentencepiece.bpe.model'''	)			,   src_dict=str(data_dir / '''dict.txt'''	)			,   )
	xmod.eval()  # disable dropout
	print(_lowercase	)

	lowerCAmelCase_      							=			xmod.model.encoder.sentence_encoder
	lowerCAmelCase_      							=			XmodConfig(
	    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings			,   hidden_size=xmod.cfg.model.encoder_embed_dim			,   num_hidden_layers=xmod.cfg.model.encoder_layers			,   num_attention_heads=xmod.cfg.model.encoder_attention_heads			,   intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim			,   max_position_embeddings=5_1_4			,   type_vocab_size=1			,   layer_norm_eps=1E-5			,   pre_norm=xmod.cfg.model.encoder_normalize_before			,   adapter_reduction_factor=getattr(xmod.cfg.model			,   '''bottleneck'''			,   2	)			,   adapter_layer_norm=xmod.cfg.model.adapter_layer_norm			,   adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm			,   ln_before_adapter=xmod.cfg.model.ln_before_adapter			,   languages=xmod.cfg.model.languages			,   )
	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

	print('''Our X-MOD config:'''			,   _lowercase	)

	lowerCAmelCase_      							=			XmodForSequenceClassification(_lowercase	) if classification_head else XmodForMaskedLM(_lowercase	)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	lowerCAmelCase_      							=			xmod_sent_encoder.embed_tokens.weight
	lowerCAmelCase_      							=			xmod_sent_encoder.embed_positions.weight
	lowerCAmelCase_      							=			torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c xmod doesn't use them.

	lowerCAmelCase_      							=			xmod_sent_encoder.layernorm_embedding.weight
	lowerCAmelCase_      							=			xmod_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers	):
		# Encoder: start of layer
		lowerCAmelCase_      							=			model.roberta.encoder.layer[i]
		lowerCAmelCase_      							=			xmod_sent_encoder.layers[i]

		# self attention
		lowerCAmelCase_      							=			layer.attention.self
		if not (
		    xmod_layer.self_attn.k_proj.weight.data.shape
		    == xmod_layer.self_attn.q_proj.weight.data.shape
		    == xmod_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)	)
		):
			raise AssertionError('''Dimensions of self-attention weights do not match.'''	)

		lowerCAmelCase_      							=			xmod_layer.self_attn.q_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.q_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn.k_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.k_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn.v_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.v_proj.bias

		# self-attention output
		lowerCAmelCase_      							=			layer.attention.output
		if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
			raise AssertionError('''Dimensions of self-attention output weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.self_attn.out_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.out_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn_layer_norm.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn_layer_norm.bias

		# intermediate
		lowerCAmelCase_      							=			layer.intermediate
		if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of intermediate weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.fca.weight
		lowerCAmelCase_      							=			xmod_layer.fca.bias

		# output
		lowerCAmelCase_      							=			layer.output
		if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of feed-forward weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.fca.weight
		lowerCAmelCase_      							=			xmod_layer.fca.bias
		lowerCAmelCase_      							=			xmod_layer.final_layer_norm.weight
		lowerCAmelCase_      							=			xmod_layer.final_layer_norm.bias
		if bert_output.adapter_layer_norm is not None:
			lowerCAmelCase_      							=			xmod_layer.adapter_layer_norm.weight
			lowerCAmelCase_      							=			xmod_layer.adapter_layer_norm.bias

		if sorted(bert_output.adapter_modules.keys()	) != sorted(xmod_layer.adapter_modules.keys()	):
			raise AssertionError('''Lists of language adapters do not match.'''	)
		for lang_code, adapter in xmod_layer.adapter_modules.items():
			lowerCAmelCase_      							=			bert_output.adapter_modules[lang_code]
			lowerCAmelCase_      							=			xmod_layer.adapter_modules[lang_code]
			lowerCAmelCase_      							=			from_adapter.fca.weight
			lowerCAmelCase_      							=			from_adapter.fca.bias
			lowerCAmelCase_      							=			from_adapter.fca.weight
			lowerCAmelCase_      							=			from_adapter.fca.bias

		# end of layer

	if xmod_sent_encoder.layer_norm is not None:
		lowerCAmelCase_      							=			xmod_sent_encoder.layer_norm.weight
		lowerCAmelCase_      							=			xmod_sent_encoder.layer_norm.bias

	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].dense.weight
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].dense.bias
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.weight
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.bias
	else:
		# LM Head
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.dense.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.dense.bias
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.layer_norm.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.layer_norm.bias
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	lowerCAmelCase_      							=			xmod.encode(_lowercase	).unsqueeze(0	)  # batch of size 1
	model.roberta.set_default_language(_lowercase	)

	lowerCAmelCase_      							=			model(_lowercase	)[0]
	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''](xmod.extract_features(_lowercase	)	)
	else:
		lowerCAmelCase_      							=			xmod.model(_lowercase			,   lang_id=[SAMPLE_LANGUAGE]	)[0]
	print(our_output.shape			,   their_output.shape	)
	lowerCAmelCase_      							=			torch.max(torch.abs(our_output - their_output	)	).item()
	print(F"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
	lowerCAmelCase_      							=			torch.allclose(_lowercase			,   _lowercase			,   atol=1E-3	)
	print('''Do both models output the same tensors?'''			,   '''🔥''' if success else '''💩'''	)
	if not success:
		raise Exception('''Something went wRoNg'''	)

	Path(_lowercase	).mkdir(parents=_lowercase			,   exist_ok=_lowercase	)
	print(F"""Saving model to {pytorch_dump_folder_path}"""	)
	model.save_pretrained(_lowercase	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
		)
		lowercase_							=							parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)